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142 | class MultiDocumentAgentsPack(BaseLlamaPack):
"""Multi-document Agents pack.
Given a set of documents, build our multi-document agents architecture.
- setup a document agent over agent doc (capable of QA and summarization)
- setup a top-level agent over doc agents
"""
def __init__(
self,
docs: List[Document],
doc_titles: List[str],
doc_descriptions: List[str],
**kwargs: Any,
) -> None:
"""Init params."""
self.node_parser = SentenceSplitter()
self.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
self.service_context = ServiceContext.from_defaults(llm=self.llm)
# Build agents dictionary
self.agents = {}
# this is for the baseline
all_nodes = []
# build agent for each document
for idx, doc in enumerate(docs):
doc_title = doc_titles[idx]
doc_description = doc_descriptions[idx]
nodes = self.node_parser.get_nodes_from_documents([doc])
all_nodes.extend(nodes)
# build vector index
vector_index = VectorStoreIndex(nodes, service_context=self.service_context)
# build summary index
summary_index = SummaryIndex(nodes, service_context=self.service_context)
# define query engines
vector_query_engine = vector_index.as_query_engine()
summary_query_engine = summary_index.as_query_engine()
# define tools
query_engine_tools = [
QueryEngineTool(
query_engine=vector_query_engine,
metadata=ToolMetadata(
name="vector_tool",
description=(
"Useful for questions related to specific aspects of"
f" {doc_title}."
),
),
),
QueryEngineTool(
query_engine=summary_query_engine,
metadata=ToolMetadata(
name="summary_tool",
description=(
"Useful for any requests that require a holistic summary"
f" of EVERYTHING about {doc_title}. "
),
),
),
]
# build agent
function_llm = OpenAI(model="gpt-4")
agent = OpenAIAgent.from_tools(
query_engine_tools,
llm=function_llm,
verbose=True,
system_prompt=f"""\
You are a specialized agent designed to answer queries about {doc_title}.
You must ALWAYS use at least one of the tools provided when answering a question; do NOT rely on prior knowledge.\
""",
)
self.agents[doc_title] = agent
# build top-level, retrieval-enabled OpenAI Agent
# define tool for each document agent
all_tools = []
for idx, doc in enumerate(docs):
doc_title = doc_titles[idx]
doc_description = doc_descriptions[idx]
wiki_summary = (
f"Use this tool if you want to answer any questions about {doc_title}.\n"
f"Doc description: {doc_description}\n"
)
doc_tool = QueryEngineTool(
query_engine=self.agents[doc_title],
metadata=ToolMetadata(
name=f"tool_{doc_title}",
description=wiki_summary,
),
)
all_tools.append(doc_tool)
tool_mapping = SimpleToolNodeMapping.from_objects(all_tools)
self.obj_index = ObjectIndex.from_objects(
all_tools,
tool_mapping,
VectorStoreIndex,
)
self.top_agent = FnRetrieverOpenAIAgent.from_retriever(
self.obj_index.as_retriever(similarity_top_k=3),
system_prompt=""" \
You are an agent designed to answer queries about a set of given cities.
Please always use the tools provided to answer a question. Do not rely on prior knowledge.\
""",
verbose=True,
)
def get_modules(self) -> Dict[str, Any]:
"""Get modules."""
return {
"top_agent": self.top_agent,
"obj_index": self.obj_index,
"doc_agents": self.agents,
}
def run(self, *args: Any, **kwargs: Any) -> Any:
"""Run the pipeline."""
return self.top_agent.query(*args, **kwargs)
|